Overview

Dataset statistics

Number of variables22
Number of observations29645
Missing cells278
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory170.0 B

Variable types

Numeric9
Text6
DateTime2
Categorical3
Boolean2

Alerts

year has constant value ""Constant
unique_key is highly overall correlated with primary_type and 1 other fieldsHigh correlation
beat is highly overall correlated with district and 6 other fieldsHigh correlation
district is highly overall correlated with beat and 6 other fieldsHigh correlation
ward is highly overall correlated with beat and 6 other fieldsHigh correlation
community_area is highly overall correlated with beat and 4 other fieldsHigh correlation
x_coordinate is highly overall correlated with beat and 5 other fieldsHigh correlation
y_coordinate is highly overall correlated with beat and 6 other fieldsHigh correlation
latitude is highly overall correlated with beat and 6 other fieldsHigh correlation
longitude is highly overall correlated with beat and 5 other fieldsHigh correlation
primary_type is highly overall correlated with unique_key and 3 other fieldsHigh correlation
arrest is highly overall correlated with primary_type and 1 other fieldsHigh correlation
domestic is highly overall correlated with primary_type and 1 other fieldsHigh correlation
fbi_code is highly overall correlated with unique_key and 3 other fieldsHigh correlation
unique_key has unique valuesUnique

Reproduction

Analysis started2023-09-23 04:45:41.405360
Analysis finished2023-09-23 04:46:42.426161
Duration1 minute and 1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

unique_key
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29645
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12886977
Minimum27279
Maximum13184975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:42.661053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum27279
5-th percentile12947954
Q112997099
median13057704
Q313120337
95-th percentile13166620
Maximum13184975
Range13157696
Interquartile range (IQR)123238

Descriptive statistics

Standard deviation1492152.4
Coefficient of variation (CV)0.11578762
Kurtosis70.145197
Mean12886977
Median Absolute Deviation (MAD)61665
Skewness-8.48399
Sum3.8203442 × 1011
Variance2.2265187 × 1012
MonotonicityNot monotonic
2023-09-23T04:46:43.217924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13023163 1
 
< 0.1%
13104182 1
 
< 0.1%
12967872 1
 
< 0.1%
13174417 1
 
< 0.1%
13002361 1
 
< 0.1%
13153308 1
 
< 0.1%
12996488 1
 
< 0.1%
13136853 1
 
< 0.1%
13087993 1
 
< 0.1%
13113566 1
 
< 0.1%
Other values (29635) 29635
> 99.9%
ValueCountFrequency (%)
27279 1
< 0.1%
27280 1
< 0.1%
27281 1
< 0.1%
27282 1
< 0.1%
27283 1
< 0.1%
27284 1
< 0.1%
27285 1
< 0.1%
27286 1
< 0.1%
27287 1
< 0.1%
27288 1
< 0.1%
ValueCountFrequency (%)
13184975 1
< 0.1%
13184952 1
< 0.1%
13184832 1
< 0.1%
13184746 1
< 0.1%
13184744 1
< 0.1%
13184660 1
< 0.1%
13184653 1
< 0.1%
13184646 1
< 0.1%
13184605 1
< 0.1%
13184584 1
< 0.1%
Distinct29633
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:44.064892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters237160
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29621 ?
Unique (%)99.9%

Sample

1st rowJG201149
2nd rowJG129035
3rd rowJG145599
4th rowJG134548
5th rowJG243483
ValueCountFrequency (%)
jg304885 2
 
< 0.1%
jg282670 2
 
< 0.1%
jg126633 2
 
< 0.1%
jg256987 2
 
< 0.1%
jg171194 2
 
< 0.1%
jg191739 2
 
< 0.1%
jg225210 2
 
< 0.1%
jg325333 2
 
< 0.1%
jg318738 2
 
< 0.1%
jg214225 2
 
< 0.1%
Other values (29623) 29625
99.9%
2023-09-23T04:46:45.086417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
J 29644
12.5%
G 29642
12.5%
2 25365
10.7%
1 25242
10.6%
3 24311
10.3%
4 15049
6.3%
0 15020
6.3%
7 14995
6.3%
6 14931
6.3%
5 14852
6.3%
Other values (5) 28109
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177870
75.0%
Uppercase Letter 59290
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 25365
14.3%
1 25242
14.2%
3 24311
13.7%
4 15049
8.5%
0 15020
8.4%
7 14995
8.4%
6 14931
8.4%
5 14852
8.3%
8 14418
8.1%
9 13687
7.7%
Uppercase Letter
ValueCountFrequency (%)
J 29644
50.0%
G 29642
50.0%
F 2
 
< 0.1%
H 1
 
< 0.1%
P 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 177870
75.0%
Latin 59290
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 25365
14.3%
1 25242
14.2%
3 24311
13.7%
4 15049
8.5%
0 15020
8.4%
7 14995
8.4%
6 14931
8.4%
5 14852
8.3%
8 14418
8.1%
9 13687
7.7%
Latin
ValueCountFrequency (%)
J 29644
50.0%
G 29642
50.0%
F 2
 
< 0.1%
H 1
 
< 0.1%
P 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 237160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
J 29644
12.5%
G 29642
12.5%
2 25365
10.7%
1 25242
10.6%
3 24311
10.3%
4 15049
6.3%
0 15020
6.3%
7 14995
6.3%
6 14931
6.3%
5 14852
6.3%
Other values (5) 28109
11.9%

date
Date

Distinct21327
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
Minimum2023-01-01 01:00:00+00:00
Maximum2023-08-16 12:00:00+00:00
2023-09-23T04:46:45.419946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:45.739906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

block
Text

Distinct13174
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:46.234272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length28
Mean length18.474886
Min length14

Characters and Unicode

Total characters547688
Distinct characters58
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6980 ?
Unique (%)23.5%

Sample

1st row008XX E 103RD ST
2nd row002XX W 104TH ST
3rd row103XX S WOODLAWN AVE
4th row104XX S WABASH AVE
5th row024XX S TRUMBULL AVE
ValueCountFrequency (%)
ave 15252
 
12.6%
s 11467
 
9.4%
st 10587
 
8.7%
w 9819
 
8.1%
n 6334
 
5.2%
e 2068
 
1.7%
rd 1211
 
1.0%
0000x 1019
 
0.8%
blvd 996
 
0.8%
dr 944
 
0.8%
Other values (1117) 61706
50.8%
2023-09-23T04:46:47.292400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91758
16.8%
X 58736
 
10.7%
0 39948
 
7.3%
E 37127
 
6.8%
A 34702
 
6.3%
S 32225
 
5.9%
T 23967
 
4.4%
N 21876
 
4.0%
V 18289
 
3.3%
R 17419
 
3.2%
Other values (48) 171641
31.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 357094
65.2%
Decimal Number 98708
 
18.0%
Space Separator 91758
 
16.8%
Lowercase Letter 128
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 58736
16.4%
E 37127
10.4%
A 34702
9.7%
S 32225
 
9.0%
T 23967
 
6.7%
N 21876
 
6.1%
V 18289
 
5.1%
R 17419
 
4.9%
L 14714
 
4.1%
W 13865
 
3.9%
Other values (16) 84174
23.6%
Lowercase Letter
ValueCountFrequency (%)
e 24
18.8%
v 11
 
8.6%
o 11
 
8.6%
a 10
 
7.8%
l 10
 
7.8%
t 9
 
7.0%
r 7
 
5.5%
n 7
 
5.5%
w 6
 
4.7%
i 5
 
3.9%
Other values (11) 28
21.9%
Decimal Number
ValueCountFrequency (%)
0 39948
40.5%
1 11179
 
11.3%
3 7180
 
7.3%
2 6871
 
7.0%
5 6461
 
6.5%
4 6377
 
6.5%
7 6073
 
6.2%
6 5857
 
5.9%
8 4752
 
4.8%
9 4010
 
4.1%
Space Separator
ValueCountFrequency (%)
91758
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 357222
65.2%
Common 190466
34.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 58736
16.4%
E 37127
10.4%
A 34702
9.7%
S 32225
 
9.0%
T 23967
 
6.7%
N 21876
 
6.1%
V 18289
 
5.1%
R 17419
 
4.9%
L 14714
 
4.1%
W 13865
 
3.9%
Other values (37) 84302
23.6%
Common
ValueCountFrequency (%)
91758
48.2%
0 39948
21.0%
1 11179
 
5.9%
3 7180
 
3.8%
2 6871
 
3.6%
5 6461
 
3.4%
4 6377
 
3.3%
7 6073
 
3.2%
6 5857
 
3.1%
8 4752
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 547688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
91758
16.8%
X 58736
 
10.7%
0 39948
 
7.3%
E 37127
 
6.8%
A 34702
 
6.3%
S 32225
 
5.9%
T 23967
 
4.4%
N 21876
 
4.0%
V 18289
 
3.3%
R 17419
 
3.2%
Other values (48) 171641
31.3%

iucr
Text

Distinct267
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:47.957975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters118580
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.1%

Sample

1st row0470
2nd row0558
3rd row4650
4th row5131
5th row0496
ValueCountFrequency (%)
031a 2807
 
9.5%
0320 1193
 
4.0%
1120 779
 
2.6%
1365 720
 
2.4%
0498 711
 
2.4%
0281 709
 
2.4%
0530 635
 
2.1%
2024 629
 
2.1%
0497 621
 
2.1%
1154 617
 
2.1%
Other values (257) 20224
68.2%
2023-09-23T04:46:49.228175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30003
25.3%
1 22482
19.0%
2 14068
11.9%
3 11487
 
9.7%
5 10896
 
9.2%
4 8468
 
7.1%
8 4753
 
4.0%
6 4682
 
3.9%
7 3635
 
3.1%
A 3327
 
2.8%
Other values (8) 4779
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113786
96.0%
Uppercase Letter 4794
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30003
26.4%
1 22482
19.8%
2 14068
12.4%
3 11487
 
10.1%
5 10896
 
9.6%
4 8468
 
7.4%
8 4753
 
4.2%
6 4682
 
4.1%
7 3635
 
3.2%
9 3312
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
A 3327
69.4%
P 609
 
12.7%
B 504
 
10.5%
R 246
 
5.1%
C 82
 
1.7%
N 12
 
0.3%
T 12
 
0.3%
E 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 113786
96.0%
Latin 4794
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30003
26.4%
1 22482
19.8%
2 14068
12.4%
3 11487
 
10.1%
5 10896
 
9.6%
4 8468
 
7.4%
8 4753
 
4.2%
6 4682
 
4.1%
7 3635
 
3.2%
9 3312
 
2.9%
Latin
ValueCountFrequency (%)
A 3327
69.4%
P 609
 
12.7%
B 504
 
10.5%
R 246
 
5.1%
C 82
 
1.7%
N 12
 
0.3%
T 12
 
0.3%
E 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30003
25.3%
1 22482
19.0%
2 14068
11.9%
3 11487
 
9.7%
5 10896
 
9.2%
4 8468
 
7.1%
8 4753
 
4.0%
6 4682
 
3.9%
7 3635
 
3.1%
A 3327
 
2.8%
Other values (8) 4779
 
4.0%

primary_type
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
ROBBERY
6040 
DECEPTIVE PRACTICE
3774 
BATTERY
3523 
OTHER OFFENSE
3160 
NARCOTICS
2987 
Other values (26)
10161 

Length

Max length33
Median length26
Mean length12.293945
Min length5

Characters and Unicode

Total characters364454
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPUBLIC PEACE VIOLATION
2nd rowASSAULT
3rd rowOTHER OFFENSE
4th rowOTHER OFFENSE
5th rowBATTERY

Common Values

ValueCountFrequency (%)
ROBBERY 6040
20.4%
DECEPTIVE PRACTICE 3774
12.7%
BATTERY 3523
11.9%
OTHER OFFENSE 3160
10.7%
NARCOTICS 2987
10.1%
CRIMINAL TRESPASS 1262
 
4.3%
OFFENSE INVOLVING CHILDREN 1128
 
3.8%
ASSAULT 1100
 
3.7%
CRIMINAL SEXUAL ASSAULT 983
 
3.3%
SEX OFFENSE 815
 
2.7%
Other values (21) 4873
16.4%

Length

2023-09-23T04:46:49.593260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
robbery 6040
12.7%
offense 5103
 
10.8%
deceptive 3774
 
8.0%
practice 3774
 
8.0%
battery 3523
 
7.4%
other 3162
 
6.7%
narcotics 2987
 
6.3%
criminal 2761
 
5.8%
assault 2083
 
4.4%
violation 1343
 
2.8%
Other values (34) 12913
27.2%

Most occurring characters

ValueCountFrequency (%)
E 48620
13.3%
R 33738
 
9.3%
T 29271
 
8.0%
I 26932
 
7.4%
C 24604
 
6.8%
A 24570
 
6.7%
O 24226
 
6.6%
S 19312
 
5.3%
N 18400
 
5.0%
17818
 
4.9%
Other values (16) 96963
26.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 346634
95.1%
Space Separator 17818
 
4.9%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 48620
14.0%
R 33738
9.7%
T 29271
 
8.4%
I 26932
 
7.8%
C 24604
 
7.1%
A 24570
 
7.1%
O 24226
 
7.0%
S 19312
 
5.6%
N 18400
 
5.3%
B 16904
 
4.9%
Other values (14) 80057
23.1%
Space Separator
ValueCountFrequency (%)
17818
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 346634
95.1%
Common 17820
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 48620
14.0%
R 33738
9.7%
T 29271
 
8.4%
I 26932
 
7.8%
C 24604
 
7.1%
A 24570
 
7.1%
O 24226
 
7.0%
S 19312
 
5.6%
N 18400
 
5.3%
B 16904
 
4.9%
Other values (14) 80057
23.1%
Common
ValueCountFrequency (%)
17818
> 99.9%
- 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 364454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 48620
13.3%
R 33738
 
9.3%
T 29271
 
8.0%
I 26932
 
7.4%
C 24604
 
6.8%
A 24570
 
6.7%
O 24226
 
6.6%
S 19312
 
5.3%
N 18400
 
5.0%
17818
 
4.9%
Other values (16) 96963
26.6%
Distinct253
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:50.107076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length60
Median length49
Mean length26.437679
Min length5

Characters and Unicode

Total characters783745
Distinct characters38
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.1%

Sample

1st rowRECKLESS CONDUCT
2nd rowAGGRAVATED PROTECTED EMPLOYEE - OTHER DANGEROUS WEAPON
3rd rowSEX OFFENDER - FAIL TO REGISTER
4th rowVIOLENT OFFENDER - ANNUAL REGISTRATION
5th rowAGGRAVATED DOMESTIC BATTERY - KNIFE / CUTTING INSTRUMENT
ValueCountFrequency (%)
19381
 
15.3%
aggravated 4995
 
4.0%
armed 3595
 
2.8%
other 3583
 
2.8%
handgun 3317
 
2.6%
weapon 2972
 
2.4%
no 2528
 
2.0%
theft 2395
 
1.9%
of 2208
 
1.7%
to 2110
 
1.7%
Other values (332) 79196
62.7%
2023-09-23T04:46:51.622705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
96722
12.3%
E 77922
 
9.9%
A 59809
 
7.6%
T 55556
 
7.1%
N 51075
 
6.5%
R 49970
 
6.4%
I 42790
 
5.5%
S 41954
 
5.4%
O 41857
 
5.3%
D 28892
 
3.7%
Other values (28) 237198
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 652634
83.3%
Space Separator 96722
 
12.3%
Dash Punctuation 14837
 
1.9%
Other Punctuation 13858
 
1.8%
Decimal Number 3555
 
0.5%
Open Punctuation 761
 
0.1%
Close Punctuation 761
 
0.1%
Currency Symbol 617
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 77922
11.9%
A 59809
 
9.2%
T 55556
 
8.5%
N 51075
 
7.8%
R 49970
 
7.7%
I 42790
 
6.6%
S 41954
 
6.4%
O 41857
 
6.4%
D 28892
 
4.4%
G 27070
 
4.1%
Other values (16) 175739
26.9%
Decimal Number
ValueCountFrequency (%)
0 2081
58.5%
3 1171
32.9%
1 298
 
8.4%
8 5
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 6685
48.2%
/ 5666
40.9%
. 1507
 
10.9%
Space Separator
ValueCountFrequency (%)
96722
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14837
100.0%
Open Punctuation
ValueCountFrequency (%)
( 761
100.0%
Close Punctuation
ValueCountFrequency (%)
) 761
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 652634
83.3%
Common 131111
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 77922
11.9%
A 59809
 
9.2%
T 55556
 
8.5%
N 51075
 
7.8%
R 49970
 
7.7%
I 42790
 
6.6%
S 41954
 
6.4%
O 41857
 
6.4%
D 28892
 
4.4%
G 27070
 
4.1%
Other values (16) 175739
26.9%
Common
ValueCountFrequency (%)
96722
73.8%
- 14837
 
11.3%
, 6685
 
5.1%
/ 5666
 
4.3%
0 2081
 
1.6%
. 1507
 
1.1%
3 1171
 
0.9%
( 761
 
0.6%
) 761
 
0.6%
$ 617
 
0.5%
Other values (2) 303
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
96722
12.3%
E 77922
 
9.9%
A 59809
 
7.6%
T 55556
 
7.1%
N 51075
 
6.5%
R 49970
 
6.4%
I 42790
 
5.5%
S 41954
 
5.4%
O 41857
 
5.3%
D 28892
 
3.7%
Other values (28) 237198
30.3%
Distinct123
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:52.311780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length53
Median length47
Mean length11.361174
Min length4

Characters and Unicode

Total characters336802
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowSIDEWALK
2nd rowSCHOOL - PUBLIC BUILDING
3rd rowSTREET
4th rowSTREET
5th rowAPARTMENT
ValueCountFrequency (%)
street 8273
16.5%
apartment 4834
 
9.7%
4513
 
9.0%
residence 4155
 
8.3%
sidewalk 2870
 
5.7%
lot 1205
 
2.4%
alley 1191
 
2.4%
cta 1171
 
2.3%
store 1137
 
2.3%
parking 1088
 
2.2%
Other values (165) 19638
39.2%
2023-09-23T04:46:53.815456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 51882
15.4%
T 37705
11.2%
R 30865
 
9.2%
A 28082
 
8.3%
S 23530
 
7.0%
20430
 
6.1%
N 19585
 
5.8%
I 18745
 
5.6%
L 15015
 
4.5%
C 12224
 
3.6%
Other values (25) 78739
23.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 308376
91.6%
Space Separator 20430
 
6.1%
Other Punctuation 3208
 
1.0%
Dash Punctuation 1940
 
0.6%
Close Punctuation 1424
 
0.4%
Open Punctuation 1424
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 51882
16.8%
T 37705
12.2%
R 30865
10.0%
A 28082
9.1%
S 23530
 
7.6%
N 19585
 
6.4%
I 18745
 
6.1%
L 15015
 
4.9%
C 12224
 
4.0%
O 11809
 
3.8%
Other values (16) 58934
19.1%
Other Punctuation
ValueCountFrequency (%)
/ 3143
98.0%
, 36
 
1.1%
. 25
 
0.8%
: 2
 
0.1%
" 2
 
0.1%
Space Separator
ValueCountFrequency (%)
20430
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1940
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1424
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1424
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 308376
91.6%
Common 28426
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 51882
16.8%
T 37705
12.2%
R 30865
10.0%
A 28082
9.1%
S 23530
 
7.6%
N 19585
 
6.4%
I 18745
 
6.1%
L 15015
 
4.9%
C 12224
 
4.0%
O 11809
 
3.8%
Other values (16) 58934
19.1%
Common
ValueCountFrequency (%)
20430
71.9%
/ 3143
 
11.1%
- 1940
 
6.8%
) 1424
 
5.0%
( 1424
 
5.0%
, 36
 
0.1%
. 25
 
0.1%
: 2
 
< 0.1%
" 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 51882
15.4%
T 37705
11.2%
R 30865
 
9.2%
A 28082
 
8.3%
S 23530
 
7.0%
20430
 
6.1%
N 19585
 
5.8%
I 18745
 
5.6%
L 15015
 
4.5%
C 12224
 
3.6%
Other values (25) 78739
23.4%

arrest
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size260.6 KiB
False
22078 
True
7567 
ValueCountFrequency (%)
False 22078
74.5%
True 7567
 
25.5%
2023-09-23T04:46:54.171954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

domestic
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size260.6 KiB
False
25063 
True
4582 
ValueCountFrequency (%)
False 25063
84.5%
True 4582
 
15.5%
2023-09-23T04:46:54.428314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

beat
Real number (ℝ)

HIGH CORRELATION 

Distinct275
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1143.2237
Minimum111
Maximum2535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:54.771590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile133
Q1613
median1032
Q31651
95-th percentile2514
Maximum2535
Range2424
Interquartile range (IQR)1038

Descriptive statistics

Standard deviation691.71607
Coefficient of variation (CV)0.60505748
Kurtosis-0.78657356
Mean1143.2237
Median Absolute Deviation (MAD)519
Skewness0.44456136
Sum33890868
Variance478471.13
MonotonicityNot monotonic
2023-09-23T04:46:57.131446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1834 360
 
1.2%
1112 325
 
1.1%
1121 308
 
1.0%
111 267
 
0.9%
123 262
 
0.9%
423 258
 
0.9%
1132 242
 
0.8%
1011 241
 
0.8%
421 237
 
0.8%
1122 235
 
0.8%
Other values (265) 26910
90.8%
ValueCountFrequency (%)
111 267
0.9%
112 109
0.4%
113 144
0.5%
114 101
 
0.3%
121 66
 
0.2%
122 154
0.5%
123 262
0.9%
124 70
 
0.2%
131 158
0.5%
132 106
 
0.4%
ValueCountFrequency (%)
2535 133
0.4%
2534 195
0.7%
2533 225
0.8%
2532 128
0.4%
2531 96
0.3%
2525 42
 
0.1%
2524 74
 
0.2%
2523 73
 
0.2%
2522 134
0.5%
2521 145
0.5%

district
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.201653
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:57.774988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median10
Q316
95-th percentile25
Maximum31
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.9106098
Coefficient of variation (CV)0.61692769
Kurtosis-0.78117875
Mean11.201653
Median Absolute Deviation (MAD)5
Skewness0.44735191
Sum332073
Variance47.756528
MonotonicityNot monotonic
2023-09-23T04:46:58.303135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
11 2629
 
8.9%
6 1886
 
6.4%
4 1815
 
6.1%
8 1783
 
6.0%
25 1709
 
5.8%
1 1554
 
5.2%
12 1534
 
5.2%
10 1534
 
5.2%
7 1458
 
4.9%
9 1349
 
4.6%
Other values (13) 12394
41.8%
ValueCountFrequency (%)
1 1554
5.2%
2 1256
4.2%
3 1310
4.4%
4 1815
6.1%
5 1090
3.7%
6 1886
6.4%
7 1458
4.9%
8 1783
6.0%
9 1349
4.6%
10 1534
5.2%
ValueCountFrequency (%)
31 2
 
< 0.1%
25 1709
5.8%
24 922
3.1%
22 786
2.7%
20 497
 
1.7%
19 1182
4.0%
18 1288
4.3%
17 851
2.9%
16 1245
4.2%
15 1087
3.7%

ward
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean23.16715
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:58.597818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median24
Q334
95-th percentile46
Maximum50
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.541886
Coefficient of variation (CV)0.58452965
Kurtosis-1.0361627
Mean23.16715
Median Absolute Deviation (MAD)12
Skewness0.15864605
Sum686767
Variance183.38268
MonotonicityNot monotonic
2023-09-23T04:46:58.992996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 1674
 
5.6%
24 1445
 
4.9%
27 1395
 
4.7%
6 1185
 
4.0%
42 1130
 
3.8%
37 1064
 
3.6%
8 985
 
3.3%
17 979
 
3.3%
16 947
 
3.2%
7 945
 
3.2%
Other values (40) 17895
60.4%
ValueCountFrequency (%)
1 433
 
1.5%
2 390
 
1.3%
3 737
2.5%
4 851
2.9%
5 604
2.0%
6 1185
4.0%
7 945
3.2%
8 985
3.3%
9 820
2.8%
10 604
2.0%
ValueCountFrequency (%)
50 322
 
1.1%
49 468
1.6%
48 275
 
0.9%
47 249
 
0.8%
46 384
 
1.3%
45 339
 
1.1%
44 407
 
1.4%
43 242
 
0.8%
42 1130
3.8%
41 444
 
1.5%

community_area
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.426581
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.2 KiB
2023-09-23T04:46:59.348478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q123
median31
Q353
95-th percentile71
Maximum77
Range76
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.990309
Coefficient of variation (CV)0.57623605
Kurtosis-0.97025554
Mean36.426581
Median Absolute Deviation (MAD)14
Skewness0.2735857
Sum1079866
Variance440.59309
MonotonicityNot monotonic
2023-09-23T04:46:59.741190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 1770
 
6.0%
23 1254
 
4.2%
8 1089
 
3.7%
29 1086
 
3.7%
32 1037
 
3.5%
43 935
 
3.2%
28 934
 
3.2%
44 801
 
2.7%
71 784
 
2.6%
26 781
 
2.6%
Other values (67) 19174
64.7%
ValueCountFrequency (%)
1 469
1.6%
2 429
 
1.4%
3 400
 
1.3%
4 208
 
0.7%
5 131
 
0.4%
6 594
2.0%
7 315
 
1.1%
8 1089
3.7%
9 46
 
0.2%
10 140
 
0.5%
ValueCountFrequency (%)
77 255
 
0.9%
76 280
 
0.9%
75 193
 
0.7%
74 66
 
0.2%
73 283
 
1.0%
72 94
 
0.3%
71 784
2.6%
70 229
 
0.8%
69 732
2.5%
68 679
2.3%

fbi_code
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
03
6040 
26
4754 
18
2985 
04B
2776 
11
2697 
Other values (21)
10393 

Length

Max length3
Median length2
Mean length2.1900152
Min length2

Characters and Unicode

Total characters64923
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row24
2nd row04A
3rd row26
4th row26
5th row04B

Common Values

ValueCountFrequency (%)
03 6040
20.4%
26 4754
16.0%
18 2985
10.1%
04B 2776
9.4%
11 2697
9.1%
02 1103
 
3.7%
17 1094
 
3.7%
08B 1083
 
3.7%
10 1021
 
3.4%
24 860
 
2.9%
Other values (16) 5232
17.6%

Length

2023-09-23T04:47:00.147322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03 6040
20.4%
26 4754
16.0%
18 2985
10.1%
04b 2776
9.4%
11 2697
9.1%
02 1103
 
3.7%
17 1094
 
3.7%
08b 1083
 
3.7%
10 1021
 
3.4%
24 860
 
2.9%
Other values (16) 5232
17.6%

Most occurring characters

ValueCountFrequency (%)
0 15710
24.2%
1 12310
19.0%
2 7357
11.3%
3 6081
 
9.4%
6 5347
 
8.2%
4 5010
 
7.7%
8 4590
 
7.1%
B 3860
 
5.9%
A 1773
 
2.7%
7 1481
 
2.3%
Other values (2) 1404
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59290
91.3%
Uppercase Letter 5633
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15710
26.5%
1 12310
20.8%
2 7357
12.4%
3 6081
 
10.3%
6 5347
 
9.0%
4 5010
 
8.4%
8 4590
 
7.7%
7 1481
 
2.5%
5 1093
 
1.8%
9 311
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
B 3860
68.5%
A 1773
31.5%

Most occurring scripts

ValueCountFrequency (%)
Common 59290
91.3%
Latin 5633
 
8.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15710
26.5%
1 12310
20.8%
2 7357
12.4%
3 6081
 
10.3%
6 5347
 
9.0%
4 5010
 
8.4%
8 4590
 
7.7%
7 1481
 
2.5%
5 1093
 
1.8%
9 311
 
0.5%
Latin
ValueCountFrequency (%)
B 3860
68.5%
A 1773
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64923
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15710
24.2%
1 12310
19.0%
2 7357
11.3%
3 6081
 
9.4%
6 5347
 
8.2%
4 5010
 
7.7%
8 4590
 
7.1%
B 3860
 
5.9%
A 1773
 
2.7%
7 1481
 
2.3%
Other values (2) 1404
 
2.2%

x_coordinate
Real number (ℝ)

HIGH CORRELATION 

Distinct18367
Distinct (%)62.1%
Missing56
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1164007.4
Minimum1091242
Maximum1205114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.2 KiB
2023-09-23T04:47:00.459699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1091242
5-th percentile1137832.4
Q11151534
median1165482
Q31176406
95-th percentile1191761.6
Maximum1205114
Range113872
Interquartile range (IQR)24872

Descriptive statistics

Standard deviation16967.153
Coefficient of variation (CV)0.0145765
Kurtosis0.361447
Mean1164007.4
Median Absolute Deviation (MAD)12013
Skewness-0.33752057
Sum3.4441814 × 1010
Variance2.8788428 × 108
MonotonicityNot monotonic
2023-09-23T04:47:00.834961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1176330 166
 
0.6%
1176278 77
 
0.3%
1145654 69
 
0.2%
1176517 62
 
0.2%
1106955 51
 
0.2%
1178046 50
 
0.2%
1100317 44
 
0.1%
1101811 37
 
0.1%
1176417 35
 
0.1%
1152896 32
 
0.1%
Other values (18357) 28966
97.7%
(Missing) 56
 
0.2%
ValueCountFrequency (%)
1091242 1
 
< 0.1%
1094587 2
 
< 0.1%
1098012 7
 
< 0.1%
1100317 44
0.1%
1100658 27
0.1%
1100726 16
 
0.1%
1100889 1
 
< 0.1%
1100955 4
 
< 0.1%
1100995 1
 
< 0.1%
1101086 1
 
< 0.1%
ValueCountFrequency (%)
1205114 1
 
< 0.1%
1204784 1
 
< 0.1%
1204706 1
 
< 0.1%
1204568 1
 
< 0.1%
1204544 7
< 0.1%
1204480 1
 
< 0.1%
1204461 1
 
< 0.1%
1204458 1
 
< 0.1%
1204403 1
 
< 0.1%
1204244 1
 
< 0.1%

y_coordinate
Real number (ℝ)

HIGH CORRELATION 

Distinct19991
Distinct (%)67.6%
Missing56
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1887179.2
Minimum1813938
Maximum1951493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.2 KiB
2023-09-23T04:47:01.217729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1813938
5-th percentile1836579.4
Q11860067
median1894121
Q31908505
95-th percentile1936100
Maximum1951493
Range137555
Interquartile range (IQR)48438

Descriptive statistics

Standard deviation31003.208
Coefficient of variation (CV)0.016428333
Kurtosis-0.88530471
Mean1887179.2
Median Absolute Deviation (MAD)25283
Skewness-0.1018173
Sum5.5839744 × 1010
Variance9.6119892 × 108
MonotonicityNot monotonic
2023-09-23T04:47:01.580404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1901649 165
 
0.6%
1903807 77
 
0.3%
1866253 69
 
0.2%
1905133 68
 
0.2%
1895340 61
 
0.2%
1941060 51
 
0.2%
1935229 44
 
0.1%
1934419 37
 
0.1%
1899156 35
 
0.1%
1852629 32
 
0.1%
Other values (19981) 28950
97.7%
(Missing) 56
 
0.2%
ValueCountFrequency (%)
1813938 1
 
< 0.1%
1814333 2
< 0.1%
1814793 3
< 0.1%
1814998 1
 
< 0.1%
1815111 3
< 0.1%
1815117 1
 
< 0.1%
1815139 1
 
< 0.1%
1815685 1
 
< 0.1%
1815748 1
 
< 0.1%
1815755 2
< 0.1%
ValueCountFrequency (%)
1951493 3
< 0.1%
1951492 3
< 0.1%
1951491 1
 
< 0.1%
1951400 1
 
< 0.1%
1951318 5
< 0.1%
1951246 1
 
< 0.1%
1951198 1
 
< 0.1%
1951167 1
 
< 0.1%
1951001 5
< 0.1%
1950997 2
 
< 0.1%

year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
2023
29645 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters118580
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 29645
100.0%

Length

2023-09-23T04:47:01.900315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-23T04:47:02.180639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2023 29645
100.0%

Most occurring characters

ValueCountFrequency (%)
2 59290
50.0%
0 29645
25.0%
3 29645
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 118580
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 59290
50.0%
0 29645
25.0%
3 29645
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 59290
50.0%
0 29645
25.0%
3 29645
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 59290
50.0%
0 29645
25.0%
3 29645
25.0%
Distinct218
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size463.2 KiB
Minimum2023-01-08 03:58:52+00:00
Maximum2023-08-23 03:41:55+00:00
2023-09-23T04:47:02.423918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:47:02.813491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct23022
Distinct (%)77.8%
Missing55
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean41.845851
Minimum36.619446
Maximum42.022537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size463.2 KiB
2023-09-23T04:47:03.159877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum36.619446
5-th percentile41.706924
Q141.771519
median41.865269
Q341.904555
95-th percentile41.980686
Maximum42.022537
Range5.4030903
Interquartile range (IQR)0.13303568

Descriptive statistics

Standard deviation0.090530745
Coefficient of variation (CV)0.0021634342
Kurtosis374.14144
Mean41.845851
Median Absolute Deviation (MAD)0.069538213
Skewness-6.5839777
Sum1238218.7
Variance0.0081958158
MonotonicityNot monotonic
2023-09-23T04:47:03.494018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.88548189 165
 
0.6%
41.89140473 77
 
0.3%
41.78898704 69
 
0.2%
41.8681654 61
 
0.2%
41.99491395 51
 
0.2%
41.89500328 50
 
0.2%
41.9790063 44
 
0.1%
41.97676298 37
 
0.1%
41.878639 35
 
0.1%
41.75094076 32
 
0.1%
Other values (23012) 28969
97.7%
(Missing) 55
 
0.2%
ValueCountFrequency (%)
36.61944639 1
 
< 0.1%
41.64461219 1
 
< 0.1%
41.64528766 2
< 0.1%
41.64703861 1
 
< 0.1%
41.64703922 1
 
< 0.1%
41.64703943 1
 
< 0.1%
41.64714143 1
 
< 0.1%
41.64751887 1
 
< 0.1%
41.64792229 1
 
< 0.1%
41.64792494 3
< 0.1%
ValueCountFrequency (%)
42.02253674 1
 
< 0.1%
42.0225353 1
 
< 0.1%
42.02253524 1
 
< 0.1%
42.0225261 1
 
< 0.1%
42.02252546 1
 
< 0.1%
42.02252105 1
 
< 0.1%
42.02252091 1
 
< 0.1%
42.0222574 1
 
< 0.1%
42.02206269 5
< 0.1%
42.02184131 1
 
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct23020
Distinct (%)77.8%
Missing55
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean-87.673776
Minimum-91.686566
Maximum-87.524652
Zeros0
Zeros (%)0.0%
Negative29590
Negative (%)99.8%
Memory size463.2 KiB
2023-09-23T04:47:03.819927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-91.686566
5-th percentile-87.769121
Q1-87.718934
median-87.667999
Q3-87.627936
95-th percentile-87.572757
Maximum-87.524652
Range4.1619139
Interquartile range (IQR)0.090997994

Descriptive statistics

Standard deviation0.066030195
Coefficient of variation (CV)-0.00075313506
Kurtosis460.66026
Mean-87.673776
Median Absolute Deviation (MAD)0.043743229
Skewness-7.8671817
Sum-2594267
Variance0.0043599866
MonotonicityNot monotonic
2023-09-23T04:47:04.134117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.62793569 165
 
0.6%
-87.62806151 77
 
0.3%
-87.74147999 69
 
0.2%
-87.62743954 61
 
0.2%
-87.88193767 51
 
0.2%
-87.62152816 50
 
0.2%
-87.90646315 44
 
0.1%
-87.90098372 37
 
0.1%
-87.62769149 35
 
0.1%
-87.62518522 32
 
0.1%
Other values (23010) 28969
97.7%
(Missing) 55
 
0.2%
ValueCountFrequency (%)
-91.68656568 1
 
< 0.1%
-87.93973294 1
 
< 0.1%
-87.92736489 2
 
< 0.1%
-87.91510545 7
 
< 0.1%
-87.90646315 44
0.1%
-87.90522722 27
0.1%
-87.90497627 16
 
0.1%
-87.90437138 1
 
< 0.1%
-87.90412276 4
 
< 0.1%
-87.90397973 1
 
< 0.1%
ValueCountFrequency (%)
-87.52465181 1
 
< 0.1%
-87.52574799 1
 
< 0.1%
-87.52602515 1
 
< 0.1%
-87.52651684 1
 
< 0.1%
-87.52685268 1
 
< 0.1%
-87.5269465 1
 
< 0.1%
-87.52700694 1
 
< 0.1%
-87.52701858 1
 
< 0.1%
-87.52725823 7
< 0.1%
-87.52771686 1
 
< 0.1%
Distinct23022
Distinct (%)77.8%
Missing55
Missing (%)0.2%
Memory size463.2 KiB
2023-09-23T04:47:04.627078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length29
Median length29
Mean length28.780635
Min length23

Characters and Unicode

Total characters851619
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19745 ?
Unique (%)66.7%

Sample

1st row(41.707519433, -87.601839023)
2nd row(41.705267858, -87.629323117)
3rd row(41.707111498, -87.594270171)
4th row(41.704889627, -87.622270378)
5th row(41.847239028, -87.7112073)
ValueCountFrequency (%)
41.885481891 165
 
0.3%
87.627935689 165
 
0.3%
41.891404732 77
 
0.1%
87.628061509 77
 
0.1%
41.788987036 69
 
0.1%
87.74147999 69
 
0.1%
41.868165405 61
 
0.1%
87.62743954 61
 
0.1%
87.881937669 51
 
0.1%
41.994913946 51
 
0.1%
Other values (46032) 58334
98.6%
2023-09-23T04:47:05.423656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 95661
11.2%
8 88199
10.4%
4 76901
9.0%
1 75887
8.9%
6 65915
 
7.7%
. 59180
 
6.9%
9 54685
 
6.4%
5 50834
 
6.0%
2 48472
 
5.7%
3 46391
 
5.4%
Other values (6) 189494
22.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 644489
75.7%
Other Punctuation 88770
 
10.4%
Open Punctuation 29590
 
3.5%
Space Separator 29590
 
3.5%
Dash Punctuation 29590
 
3.5%
Close Punctuation 29590
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 95661
14.8%
8 88199
13.7%
4 76901
11.9%
1 75887
11.8%
6 65915
10.2%
9 54685
8.5%
5 50834
7.9%
2 48472
7.5%
3 46391
7.2%
0 41544
6.4%
Other Punctuation
ValueCountFrequency (%)
. 59180
66.7%
, 29590
33.3%
Open Punctuation
ValueCountFrequency (%)
( 29590
100.0%
Space Separator
ValueCountFrequency (%)
29590
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29590
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 851619
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 95661
11.2%
8 88199
10.4%
4 76901
9.0%
1 75887
8.9%
6 65915
 
7.7%
. 59180
 
6.9%
9 54685
 
6.4%
5 50834
 
6.0%
2 48472
 
5.7%
3 46391
 
5.4%
Other values (6) 189494
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 851619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 95661
11.2%
8 88199
10.4%
4 76901
9.0%
1 75887
8.9%
6 65915
 
7.7%
. 59180
 
6.9%
9 54685
 
6.4%
5 50834
 
6.0%
2 48472
 
5.7%
3 46391
 
5.4%
Other values (6) 189494
22.3%

Interactions

2023-09-23T04:46:37.592739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:15.183282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:18.466072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:20.898623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:23.581836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:26.020608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:29.263608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:32.278665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:35.145807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:37.873964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:15.697681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:18.738945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:21.172625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:23.858980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:26.326434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:29.656688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:32.551950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:35.423525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:38.153759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:16.114005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:18.992337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:21.437490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:24.114993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:26.648958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:30.069688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:32.831796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:35.691312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:38.433449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:16.539945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:19.258350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:21.712676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:24.403652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:26.947742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:30.445391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:33.093673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:35.976084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:38.688566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:16.995336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:19.525231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:21.999004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:24.679859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:27.222553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:30.850663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:33.659352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:36.224852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:38.952575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:17.382823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:19.815843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:22.276819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:24.959161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:27.609715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:31.196756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:33.962042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:36.506018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:39.217404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:17.651282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:20.100400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:22.575672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:25.220418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:27.999917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:31.466684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:34.246556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:36.777497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:39.494084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:17.924397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:20.385795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:23.077060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:25.490788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:28.431705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:31.736435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:34.551159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:37.065181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:39.767564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:18.189989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:20.656833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:23.331140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:25.765520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:28.867607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:32.020679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:34.861297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-23T04:46:37.334219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-23T04:47:05.730791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
unique_keybeatdistrictwardcommunity_areax_coordinatey_coordinatelatitudelongitudeprimary_typearrestdomesticfbi_code
unique_key1.0000.0090.0090.000-0.0170.0230.0140.0140.0240.9980.0120.0201.000
beat0.0091.0000.9990.678-0.602-0.6270.6960.697-0.6230.1310.1210.1320.128
district0.0090.9991.0000.684-0.607-0.6350.7030.704-0.6300.1220.1130.1220.119
ward0.0000.6780.6841.000-0.602-0.5470.7300.730-0.5410.1230.1400.1360.121
community_area-0.017-0.602-0.607-0.6021.0000.366-0.820-0.8190.3560.1220.1330.1430.120
x_coordinate0.023-0.627-0.635-0.5470.3661.000-0.574-0.5771.0000.1320.1520.0870.125
y_coordinate0.0140.6960.7030.730-0.820-0.5741.0001.000-0.5630.1190.1600.1450.114
latitude0.0140.6970.7040.730-0.819-0.5771.0001.000-0.5660.0000.0000.0000.000
longitude0.024-0.623-0.630-0.5410.3561.000-0.563-0.5661.0000.0000.0000.0000.000
primary_type0.9980.1310.1220.1230.1220.1320.1190.0000.0001.0000.6600.6050.855
arrest0.0120.1210.1130.1400.1330.1520.1600.0000.0000.6601.0000.1050.644
domestic0.0200.1320.1220.1360.1430.0870.1450.0000.0000.6050.1051.0000.581
fbi_code1.0000.1280.1190.1210.1200.1250.1140.0000.0000.8550.6440.5811.000

Missing values

2023-09-23T04:46:40.235295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-23T04:46:41.049109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-23T04:46:41.959778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

unique_keycase_numberdateblockiucrprimary_typedescriptionlocation_descriptionarrestdomesticbeatdistrictwardcommunity_areafbi_codex_coordinatey_coordinateyearupdated_onlatitudelongitudelocation
013023163JG2011492023-03-27 04:00:00.000000 UTC008XX E 103RD ST0470PUBLIC PEACE VIOLATIONRECKLESS CONDUCTSIDEWALKTrueFalse51259.050241183988.01836860.020232023-08-19 03:40:26.000000 UTC41.707519-87.601839(41.707519433, -87.601839023)
112965099JG1290352023-01-25 10:15:00.000000 UTC002XX W 104TH ST0558ASSAULTAGGRAVATED PROTECTED EMPLOYEE - OTHER DANGEROUS WEAPONSCHOOL - PUBLIC BUILDINGFalseFalse512534.04904A1176490.01835977.020232023-08-19 03:40:26.000000 UTC41.705268-87.629323(41.705267858, -87.629323117)
212976908JG1455992023-02-08 05:05:00.000000 UTC103XX S WOODLAWN AVE4650OTHER OFFENSESEX OFFENDER - FAIL TO REGISTERSTREETTrueFalse51259.050261186056.01836729.020232023-08-19 03:40:26.000000 UTC41.707111-87.594270(41.707111498, -87.594270171)
312967819JG1345482023-01-30 10:00:00.000000 UTC104XX S WABASH AVE5131OTHER OFFENSEVIOLENT OFFENDER - ANNUAL REGISTRATIONSTREETTrueFalse51259.049261178417.01835855.020232023-08-19 03:40:26.000000 UTC41.704890-87.622270(41.704889627, -87.622270378)
413058907JG2434832023-05-01 04:30:00.000000 UTC024XX S TRUMBULL AVE0496BATTERYAGGRAVATED DOMESTIC BATTERY - KNIFE / CUTTING INSTRUMENTAPARTMENTTrueTrue10241022.03004B1153756.01887538.020232023-08-19 03:40:26.000000 UTC41.847239-87.711207(41.847239028, -87.7112073)
513155807JG3589712023-07-28 01:55:00.000000 UTC032XX S HAMLIN AVE1020ARSONBY FIRERESIDENCE - GARAGEFalseFalse10311022.030091151556.01882793.020232023-08-19 03:40:26.000000 UTC41.834262-87.719406(41.834261597, -87.719405832)
613024359JG2021672023-03-23 03:29:00.000000 UTC030XX S KARLOV AVE1751OFFENSE INVOLVING CHILDRENCRIMINAL SEXUAL ABUSE BY FAMILY MEMBERRESIDENCEFalseTrue10311022.030171149531.01884039.020232023-08-19 03:40:26.000000 UTC41.837720-87.726804(41.837720261, -87.72680386)
712983474JG1532112023-02-15 01:17:00.000000 UTC028XX S TRUMBULL AVE0454BATTERYAGGRAVATED P.O. - HANDS, FISTS, FEET, NO / MINOR INJURYAPARTMENTTrueTrue10321022.03008B1153830.01884872.020232023-08-19 03:40:26.000000 UTC41.839922-87.711007(41.83992173, -87.711006616)
812991723JG1628722023-02-16 01:31:00.000000 UTC027XX S DRAKE AVE1753OFFENSE INVOLVING CHILDRENSEXUAL ASSAULT OF CHILD BY FAMILY MEMBERRESIDENCEFalseTrue10321022.030021153142.01885665.020232023-08-19 03:40:26.000000 UTC41.842111-87.713510(41.842111471, -87.713510304)
913120819JG3175862023-06-27 12:16:00.000000 UTC031XX W CERMAK RD0326ROBBERYAGGRAVATED VEHICULAR HIJACKINGALLEYFalseFalse10331024.030031155548.01889204.020232023-08-19 03:40:26.000000 UTC41.851775-87.704586(41.851774912, -87.704585861)
unique_keycase_numberdateblockiucrprimary_typedescriptionlocation_descriptionarrestdomesticbeatdistrictwardcommunity_areafbi_codex_coordinatey_coordinateyearupdated_onlatitudelongitudelocation
2979913143547JG3444022023-07-16 12:50:00.000000 UTC0000X E ROOSEVELT RD0320ROBBERYSTRONG ARM - NO WEAPONCTA STATIONFalseFalse12314.032031176818.01895074.020232023-08-19 03:40:26.000000 UTC41.867429-87.626343(41.867428687, -87.626342565)
2980013125776JG3234762023-07-01 03:00:00.000000 UTC034XX N HALSTED ST0320ROBBERYSTRONG ARM - NO WEAPONOTHER (SPECIFY)FalseFalse19241944.06031170308.01923273.020232023-08-19 03:40:26.000000 UTC41.944953-87.649416(41.944953005, -87.649416212)
2980113109870JG3043702023-06-16 10:30:00.000000 UTC025XX N CLARK ST0320ROBBERYSTRONG ARM - NO WEAPONSIDEWALKFalseFalse19351943.07031172221.01917506.020232023-08-19 03:40:26.000000 UTC41.929086-87.642556(41.929086034, -87.642555646)
2980213078767JG2667012023-05-19 12:10:00.000000 UTC014XX W 37TH ST0320ROBBERYSTRONG ARM - NO WEAPONCOMMERCIAL / BUSINESS OFFICEFalseFalse912911.059031167532.01880233.020232023-08-19 03:40:26.000000 UTC41.826908-87.660859(41.826908418, -87.660859334)
2980313045333JG2273252023-04-12 07:45:00.000000 UTC001XX W 35TH ST0320ROBBERYSTRONG ARM - NO WEAPONCTA PLATFORMFalseFalse915911.034031175739.01881773.020232023-08-19 03:40:26.000000 UTC41.830954-87.630703(41.830954079, -87.630703226)
2980412968396JG1349902023-01-30 02:50:00.000000 UTC079XX S KENWOOD AVE0320ROBBERYSTRONG ARM - NO WEAPONSTREETFalseFalse41148.045031186588.01852614.020232023-08-19 03:40:26.000000 UTC41.750689-87.591821(41.75068911, -87.591820898)
2980512955391JG1194662023-01-17 01:09:00.000000 UTC116XX S AVENUE O0320ROBBERYSTRONG ARM - NO WEAPONBANKFalseFalse433410.055031200918.01828489.020232023-08-19 03:40:26.000000 UTC41.684137-87.540125(41.684137282, -87.540124611)
2980613164652JG3696022023-08-04 04:15:00.000000 UTC030XX W AUGUSTA BLVD0320ROBBERYSTRONG ARM - NO WEAPONPARK PROPERTYFalseFalse12111236.023031155907.01906501.020232023-08-19 03:40:26.000000 UTC41.899232-87.702802(41.899232417, -87.702801861)
2980713157613JG3613422023-07-29 08:30:00.000000 UTC034XX W OGDEN AVE0320ROBBERYSTRONG ARM - NO WEAPONSTREETFalseFalse10241024.029031153456.01890528.020232023-08-19 03:40:26.000000 UTC41.855450-87.712229(41.855449899, -87.712228937)
2980812980697JG1498352023-02-12 10:15:00.000000 UTC008XX W 119TH ST0320ROBBERYSTRONG ARM - NO WEAPONGROCERY FOOD STOREFalseFalse524534.053031172868.01825938.020232023-08-19 03:40:26.000000 UTC41.677800-87.642881(41.677799762, -87.642881123)